Chapter 13: Availability Heuristic and Observed Bias in Growth Forecasts: Evidence from an Analysis of Multiple Business Cycles
Byunghwan Lee, John O’Brien and K. Sivaramakrishnan INTRODUCTION A conclusion emerging from recent advances in behavioral finance research is that the trading crowd in real-world markets is boundedly rational. A direct implication of this research, especially relevant to social and computer scientists, is that the efficacy of extant decision rules and trading heuristics can be improved upon. In fact, recent evidence demonstrates that algorithmic trading can take advantage of a human trading crowd. This reinforces the fact that the human trading crowd is boundedly rational, which is currently raising serious regulatory, ethical and economic questions in relation to the integrity of the financial markets.1 In a recent paper that appeared in the Journal of Behavioral Finance, Lee, O’Brien and Sivaramakrishnan (2008) (hereafter LOS), we examined these issues by formally testing two contrasting theoretical approaches, rational versus boundedly rational, to understanding the growth forecasting behavior of financial analysts as well as related decision making by managers. By drawing upon the work of Sargent (2001), we were able to exploit hypothesized drivers of the boundedly rational behavior to identify the nature of the bounded rationality as well as to construct a forecast rule that improves upon growth forecasts issued by analysts. Given the central importance of growth forecasts to the financial markets, this result reinforces current observed trends. In this chapter, we extend the original results to one additional completed business cycle to provide a more robust ex ante test of the original results. In LOS, we show that the availability heuristic...
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